op {
  graph_op_name: "AvgPool3DGrad"
  in_arg {
    name: "orig_input_shape"
    description: <<END
The original input dimensions.
END
  }
  in_arg {
    name: "grad"
    description: <<END
Output backprop of shape `[batch, depth, rows, cols, channels]`.
END
  }
  out_arg {
    name: "output"
    description: <<END
The backprop for input.
END
  }
  attr {
    name: "ksize"
    description: <<END
1-D tensor of length 5. The size of the window for each dimension of
the input tensor. Must have `ksize[0] = ksize[4] = 1`.
END
  }
  attr {
    name: "strides"
    description: <<END
1-D tensor of length 5. The stride of the sliding window for each
dimension of `input`. Must have `strides[0] = strides[4] = 1`.
END
  }
  attr {
    name: "padding"
    description: <<END
The type of padding algorithm to use.
END
  }
  attr {
    name: "data_format"
    description: <<END
The data format of the input and output data. With the
default format "NDHWC", the data is stored in the order of:
    [batch, in_depth, in_height, in_width, in_channels].
Alternatively, the format could be "NCDHW", the data storage order is:
    [batch, in_channels, in_depth, in_height, in_width].
END
  }
  summary: "Computes gradients of average pooling function."
}
